Marginal structured SVM with hidden variables

31st International Conference on Machine Learning, ICML 2014

Volumn 2, Issue , 2014, Pages 1416-1427

  Ping, Wei ^a   Liu, Qiang ^a   Ihler, Alexander ^a  

^a Irvine   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

GRADIENT-BASED OPTIMIZATION; HIDDEN CONDITIONAL RANDOM FIELDS; HIDDEN VARIABLE; OBJECTIVE FUNCTIONS; REAL-WORLD DATASETS; STATE-OF-ART METHODS; STRUCTURED PREDICTION; UNIFIED FRAMEWORK;

LEARNING SYSTEMS;

EID: 84919912591     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (32)

1. Dhillon, P., Keerthi, S. Sathiya, Bellare, K., Chapelle, O., and S. Sundararajan. Deterministic annealing for semi-supervised structured output learning. In Proceedings ofAISTAT, pp. 299-307, 2012.
2. Fergus, R., Singh, B., Hertzmann, A., Roweis, S. T., and Freeman, W. T. Removing camera shake from a single photograph. In Proceeding of ACM SIGGRAPH, pp. 787-794, 2006.
3. Getoor, L. and Taskar, B. Introduction to statistical relational learning. The MIT press, 2007.
4. Hazan, T. and Urtasun, R. A primal-dual message-passing algorithm for approximated large scale structured prediction. In Proceedings of NIPS, 2010.
5. Koller, D. and Friedman, N. Probabilistic graphical models: principles and techniques. The MIT press, 2009.
6. Kumar, P., Packer, B., and Koller, D. Modeling latent variable uncertainty for loss-based learning. In Proceedings of lCML, pp. 465-472, 2012.
7. Lafferty, J., McCallum, A., and Pereira, F. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of lCML, pp. 282- 289, 2001.
8. Levin, A., Weiss, Y., Durand, F., and Freeman, W.T. Efficient marginal likelihood optimization in blind deconvolution. In Proceedings of CVPR, pp. 2657-2664, 2011.
9. Li, M. H., Lin, L., Wang, X.L., and Liu, T. Protein-protein interaction site prediction based on conditional random field. Bioinformatics, 23, 2007.
10. Liu, Q. and Ihler, A. Variational algorithms for marginal map. JMLR, 14:3165-3200, 2013.
11. Miller, K., Kumar, P., Packer, B., Goodman, D., and Koller, D. Max-margin min-entropy models. In Proceedings of AISTATS, pp. 779-787, 2012.
12. Naradowsky, J., Riedel, S., and Smith, D. Improving NLP through marginalization of hidden syntactic structure. In Proceeding of EMNLP, pp. 810-820, 2012.
13. Nowozin, S. and Lampert, C. Structured prediction and learning in computer vision. Foundations and Trends in Computer Graphics and Vision, 6, 2011.
14. Quattoni, A., Collins, M., and Darrell, T. Conditional random fields for object recognition. In Proceedings of NIPS, pp. 1097-1104, 2004.
15. Quattoni, A., Wang, S., Morency, L., Collins, M., and Darrell, T. Hidden conditional random fields. IEEE Transactions on PAMI, 29:1848-1852, 2007.
16. Ratliff, N., Bagnell, J. A., and Zinkevich, M. (Online) Sub- gradient methods for structured prediction. In Proceedings of AISTATS, pp. 380-387, 2007.
17. Samdani, R., Chang, M.W., and Roth, D. Unified expectation maximization. In Proceedings of NAACL, pp. 688- 698, 2012.
18. Sato, K. and Sakakibara, Y. RNA secondary structural alignment with conditional random fields. Bioinformatics, 21, 2005.
19. Schwing, A., Hazan, T., Pollefeys, M., and Urtasun, R. Efficient structured prediction with latent variables for general graphical models. In Proceedings of lCML, pp. 959-966, 2012.
20. Taskar, B., Guestrin, C., and Koller, D. Max-margin Markov networks. In Proceedings of NIPS, 2003.
21. Tsochantaridis, I., Joachims, T., Hofmann, T., and Altun, Y. Large margin methods for structured and interdependent output variables. JMLR, 6:1453-1484, 2005.
22. Verbeek, J. and Triggs, B. Scene segmentation with CRFs learned from partially labeled images. In Proceedings of NIPS, pp. 1553-1560, 2007.
23. Volkovs, M., Larochelle, H., and Zemel, R. S. Loss- sensitive training of probabilistic conditional random fields. Technical report, 2011.
24. Wainwright, M. and Jordan, M.I. Graphical models, exponential families, and variational inference. Foundations and Trends in Machine Learning, 1-2:1-305, 2008.
25. Wang, S.B, Quattoni, A., Morency, L., and Demirdjian, D. Hidden conditional random fields for gesture recognition. In Proceedings of CVPR, pp. 1521-1527, 2006.
26. Wang, Y. and Mori, G. Max-margin hidden conditional random fields for human action recognition. In Proceedings of CVPR, pp. 872-879, 2009.
27. Winn, J., Criminisi, A., and Minka, T. Object categorization by learned universal visual dictionary. In Proceedings of lCCV, pp. 1800-1807, 2005.
28. Xu, Y., Rockmore, D., and Kleinbaum, A. Hyperlink prediction in hypernetworks using latent social features. In Discovery Science, pp. 324-339, 2013.
29. Yessenalina, A., Yue, Y., and Cardie, C. Multi-level structured models for document-level sentiment classification. In Proceedings of EMNLP, pp. 1046-1056, 2010.
30. Yu, C. and Joachims, T. Learning structural SVMs with latent variables. In Proceedings of ICML, pp. 1169-1176, 2009.
31. Yuille, A. L. and Rangarajan, A. The concave-convex procedure. Neural Computation, 15:915-936, 2003.
32. Zhu, L., Chen, Y., Yuille, A., and Freeman, W. Latent hierarchical structural learning for object detection. In Proceedings of CVPR, pp. 1062-1069, 2010.